國立虎尾科技大學 |

Leveraging Cross-Task Transfer in Sequential Decision Problems.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Leveraging Cross-Task Transfer in Sequential Decision Problems./
Author:	Zentner, K. R.
Description:	1 online resource (111 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
Contained By:	Dissertations Abstracts International85-12B.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9798383057247

Leveraging Cross-Task Transfer in Sequential Decision Problems.
Zentner, K. R.

Leveraging Cross-Task Transfer in Sequential Decision Problems. - 1 online resource (111 pages)

Source: Dissertations Abstracts International, Volume: 85-12, Section: B.

Thesis (Ph.D.)--University of Southern California, 2024.

Includes bibliographical references

The past few years have seen an explosion of interest in using machine learning to make robots capable of learning a diverse set of tasks. These robots use Reinforcement Learning to learn detailed sub-second interactions, but consequently require large amounts of data for each task. In this thesis we explore how Reinforcement Learning can be combined with Transfer Learning to re-use data across tasks. We begin by reviewing the start of Multi-Task and Meta RL and describe the motivations for using Transfer Learning. Then, we describe a basic framework for using Transfer Learning to efficiently learn multiple tasks, and show how it requires predicting how effectively transfer can be performed across tasks. Next, we present a simple rule, based in information theory, for predicting the effectiveness of Cross-Task Transfer. We discuss the theoretical implications of that rule, and show various quantitative evaluations of it. Then, we show two directions of work making use of our insights to perform efficient Transfer Reinforcement Learning. The first of these directions uses Cross-Task Co-Learning and Plan Conditioned Behavioral Cloning to share skill representations produced by a Large Language Model, and it able to learn many tasks from a single demonstration each in a simulated environment. The second of these directions uses Two-Phase KL Penalization to enforce a (potentially off-policy) trust region. These advances in Transfer RL may enable robots to be used in a wider range of applications, and may also inform applying Transfer RL outside of robotics.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798383057247Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Imitation learningIndex Terms--Genre/Form:

554714
Electronic books.

Leveraging Cross-Task Transfer in Sequential Decision Problems.
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